Abstract

Although knee measurements yield high classification rates in metric sex estimation, there is a paucity of studies exploring the knee in artificial intelligence-based sexing. This proof-of-concept study aimed to develop deep learning algorithms for sex estimation from radiographs of reconstructed cadaver knee joints belonging to the Terry Anatomical Collection. A total of 199 knee radiographs were obtained from 100 skeletons (46 male and 54 female cadavers; mean age at death 64.2 years, range 50–102 years) whose tibiofemoral joints were reconstructed in standard anatomical position. The AIDeveloper software was used to train, validate, and test neural network architectures in sex estimation based on image classification. Of the explored algorithms, an MhNet-based model reached the highest overall testing accuracy of 90.3%. The model was able to classify all females (100.0%) and most males (78.6%) correctly. These preliminary findings encourage further research on artificial intelligence-based methods in sex estimation from the knee joint. Combining radiographic data with automated and externally validated algorithms may establish valuable tools to be utilized in forensic anthropology.